{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BLM calculator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Steps in this pipeline:\n",
    "1. Run the same scenario 6 times, 10 runs each, modifying the BLM value according to this rule of thumb: \n",
    "BLM = Z*sqrt(PU area)\n",
    "(Z = 0.001, 0.01, 0.1,1, 10 and 100)  \n",
    "\n",
    "2. Spatial plot the best solution of each of these scenario outputs (Visually inspect changes in BLM)\n",
    "3. Plot Boundary Length vs Cost (Curve)\n",
    "4. Calculate the optimum BLM value that minimizes both cost and Boundary length (clumping)\n",
    "\n",
    "REQUIREMENTS:\n",
    "- PU area --> To calculate rule of thumb of which BLM's to test\n",
    "- Score (output_sum.csv) --> To get best solution \n",
    "- Cost (output_sum.csv) --> To plot optimum BLM (x axis)\n",
    "- Boundary Length (output_sum.csv, it is called Connectivity_Edge in this file) --> To plot optimum BLM (y axis)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to bring all functions that we have developed for marxan.\n",
    "%run marxan_utils.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Estimate BLM testing values (6 scenarios keeping all inputs equal except BLM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import numpy as np\n",
    "from shutil import copyfile\n",
    "from distutils.dir_util import copy_tree\n",
    "import kneed\n",
    "import os\n",
    "from scipy.interpolate import splrep, splev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "MARXAN_FOLDER = '/home/jovyan/work/datasets/raw/Marxan_BLM_2/Marxan_BLM_source'\n",
    "path = '/home/jovyan/work/datasets/raw/Marxan_BLM_2/Marxan_BLM_source'\n",
    "grid_file_path = f'{MARXAN_FOLDER}/pu/pulayer.shp'\n",
    "MARXAN_INPUTDATA = 'input.dat'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createBlmProject(path: str, grid_file_path:str) -> list:\n",
    "    # copy_tree(f'{path}', f'{path}/blm')\n",
    "    # BLM values to start calibration\n",
    "    # Rule of thumb: Z = pu_area*[0.01,0.1,1,10,100]\n",
    "    MARXAN_INPUTDATA = 'input.dat'\n",
    "    \n",
    "    ### RUN MARXAN several times\n",
    "    blm_range= [0.001,0.01,0.1,1,10,100]\n",
    "    pu_area = gpd.read_file(grid_file_path)\n",
    "    area = pu_area['geometry'].map(lambda p: p.area / 10**6).mean()\n",
    "    blm_values = [element * math.sqrt(area) for element in blm_range]\n",
    "    blm_dict = dict(zip(blm_range, blm_values))\n",
    "    \n",
    "    blm_folder=[]\n",
    "\n",
    "    for blm in blm_range:\n",
    "        print(f'\\033[1m --> Running BLM_{blm}...\\033[0m')\n",
    "        \n",
    "        ## Create a folder for each BLM run at the same level as the original folder\n",
    "        if not os.path.exists(f'{os.path.dirname(path)}/BLM_{blm}'):\n",
    "            os.mkdir(f'{os.path.dirname(path)}/BLM_{blm}')\n",
    "            copy_tree(path, f'{os.path.dirname(path)}/BLM_{blm}')\n",
    "            \n",
    "        ## Read input files\n",
    "        InputFile = DatFile(f'{os.path.dirname(path)}/BLM_{blm}/{MARXAN_INPUTDATA}')\n",
    "        InputFile.read()\n",
    "        userInputFile = inputDatFile.from_dat(InputFile.data)\n",
    "\n",
    "        ## Modify for BLM calculations and save as new input.dat\n",
    "        userInputFile.BLM = blm_dict[blm]\n",
    "        userInputFile.OUTPUTDIR = 'output'\n",
    "        userInputFile.NUMREPS = 10\n",
    "        userInputFile.VERBOSITY = 0\n",
    "\n",
    "        userInputFile_df = pd.DataFrame.from_dict(userInputFile.__dict__, orient='index')\n",
    "        userInputFile_df.drop('BLOCKDEFNAME', inplace=True)\n",
    "\n",
    "        CreateFileFromDF(f'{os.path.dirname(path)}/BLM_{blm}/{MARXAN_INPUTDATA}',userInputFile_df, inputDatFile)\n",
    "        if not os.path.exists(f'{path}/{userInputFile.OUTPUTDIR}'):\n",
    "            os.mkdir(f'{path}/{userInputFile.OUTPUTDIR}')\n",
    "\n",
    "        blm_folder.append(f'BLM_{blm}')\n",
    "        os.chmod(f'{os.path.dirname(path)}/BLM_{blm}/marxan', 0o755)\n",
    "        execute_marxan(f'{os.path.dirname(path)}/BLM_{blm}')\n",
    "    \n",
    "    return blm_folder, blm_values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotBLM(path: str, grid_file_path:str):\n",
    "    blm_folder = [filename for filename in os.listdir(os.path.dirname(path)) if filename.startswith(\"BLM\")]\n",
    "    fig = plt.figure(figsize=(10,10))\n",
    "\n",
    "    for idx, folder in enumerate(blm_folder):\n",
    "        axn = fig.add_subplot(321+idx)\n",
    "        solution = pd.read_csv(f'{os.path.dirname(path)}/{folder}/output/output_best.csv')\n",
    "        pu_area = gpd.read_file(grid_file_path)\n",
    "        puid_list = ['PIUD','PU_ID','puid','pu_id']\n",
    "        for option in puid_list:\n",
    "            for col_name in pu_area.columns:\n",
    "                if option in col_name:\n",
    "                    pu_col = col_name\n",
    "\n",
    "        solution_grid = pu_area.merge(solution,left_on=f'{pu_col}',right_on = 'PUID',how='inner')\n",
    "        solution_grid.plot(ax=axn,column='SOLUTION', legend=True)\n",
    "        blm = readInput(f'{os.path.dirname(path)}/{folder}').BLM\n",
    "        axn.set_title(f'BLM = {blm}')\n",
    "        \n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Calculate optimum BLM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- For each BLM scenario infer best solution from lowest score  \n",
    "- For each BLM scenario calculate Boundary Length of the best solution\n",
    "- Create df with values for best solution of each BLM scenario \n",
    "- Find the point of max curvature to establish as optimum BLM with [kneed](https://www.kaggle.com/kevinarvai/knee-elbow-point-detection) package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.interpolate import splrep, splev\n",
    "\n",
    "def BLM_calibration(path: str,MARXAN_INPUTDATA :str, grid_file_path: str, Plot: bool = True)-> float:\n",
    "    blm_df =pd.DataFrame(columns=['folder','blm'])\n",
    "    blm_folder, blm_values = createBlmProject(path, grid_file_path)\n",
    "    MARXAN_INPUTDATA = 'input.dat'\n",
    "    blm_df['folder'] = blm_folder\n",
    "    blm_df['blm']= blm_values\n",
    "\n",
    "    for blm in blm_folder:\n",
    "        solution = validateFile(f'{os.path.dirname(MARXAN_FOLDER)}/{blm}',MARXAN_INPUTDATA, OutputRun)\n",
    "        summary = validateFile(f'{os.path.dirname(MARXAN_FOLDER)}/{blm}',MARXAN_INPUTDATA, OutputSum)\n",
    "        best =summary.loc[summary.loc[:]['Score'].idxmin(),'Run_Number']\n",
    "        cost=summary.loc[best-1,'Cost']\n",
    "        blm_df.loc[blm_df['folder']==blm,'cost']=cost\n",
    "    \n",
    "        pu_area = gpd.read_file(grid_file_path)\n",
    "        puid_list = ['PUID','PU_ID','puid','pu_id']\n",
    "        pu_col = list(set(pu_area.columns) & set(puid_list))[0]\n",
    "        \n",
    "        solution_grid = pu_area.merge(solution,left_on=f'{pu_col}',right_on = 'PUID',how='inner')\n",
    "        blm_df.loc[blm_df['folder']==blm,'boundary_length']=solution_grid.dissolve(by='SOLUTION')['geometry'].length[1] ## perimeter in m\n",
    "    \n",
    "    ###Curve with no fit\n",
    "    y = blm_df['boundary_length']\n",
    "    x = blm_df['cost']\n",
    "    kn = kneed.KneeLocator(x, y, curve='convex', direction='decreasing')\n",
    "    best_blm = blm_df.loc[blm_df['cost']==kn.knee,'blm'].values[0]\n",
    "    #blm_df.loc[blm_df['cost']==kn.knee,'folder'].values[0] #in which folder\n",
    "    \n",
    "#   ##Curve fit with polynomial\n",
    "#     y = blm_df['boundary_length'].values\n",
    "#     x = blm_df['cost'].values\n",
    "#     fit = np.polyfit(x, y, 3)\n",
    "#     fit_equation = fit[0]*x**3 + fit[1]*x**2 + fit[2]*x +fit[3]\n",
    "#     kn_p = kneed.KneeLocator(x, fit_equation, curve='convex', direction='decreasing')\n",
    "#     #best_blm_poly = blm_df.loc[blm_df['cost']==kn.knee,'blm'].values[0]\n",
    "#     best_blm_poly = kn.knee\n",
    "\n",
    "    \n",
    "#    ##Curve fit with spline\n",
    "#     blm_df = blm_df.sort_values('cost')\n",
    "#     y_sp = blm_df['boundary_length'].values\n",
    "#     x_sp = blm_df['cost'].values\n",
    "#     spl = splrep(x_sp,y_sp)\n",
    "#     y_spl = splev(x_sp,spl)\n",
    "#     kn_sp = kneed.KneeLocator(x_sp, y_spl, curve='convex', direction='decreasing')\n",
    "#     #best_blm_sp = blm_df.loc[blm_df['cost']==kn.knee,'blm'].values[0]\n",
    "#     best_blm_sp = kn.knee\n",
    "\n",
    "    \n",
    "#     print(f'The optimun BLM is poly= {best_blm_poly}, spl = {best_blm_sp}')\n",
    "    print(f'The optimun BLM is {best_blm}')\n",
    "    fig = plt.figure(figsize=(10,10))\n",
    "    if Plot==True:\n",
    "        plt.xlabel('cost')\n",
    "        plt.ylabel('boundary length')\n",
    "        plt.plot(x, y, 'bx-')\n",
    "        for xi in blm_df.cost:\n",
    "             plt.text(blm_df.loc[(blm_df['cost']==xi),'cost'].values[0], \n",
    "                      blm_df.loc[(blm_df['cost']==xi),'boundary_length'].values[0],\n",
    "                      blm_df.loc[(blm_df['cost']==xi),'blm'].values[0])\n",
    "        \n",
    "#         plt.plot(x_sp,y_spl,'--')\n",
    "#         plt.plot(x, fit_equation,color = 'r',alpha = 0.5, label = 'Polynomial fit')\n",
    "#         plt.vlines(kn_p.knee, plt.ylim()[0], plt.ylim()[1], linestyles='dashed')\n",
    "#         plt.vlines(kn_sp.knee, plt.ylim()[0], plt.ylim()[1], linestyles='dashed')\n",
    "        plt.vlines(kn.knee, plt.ylim()[0], plt.ylim()[1], linestyles='dashed')\n",
    "        \n",
    "    return blm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "DriverError",
     "evalue": "/home/jovyan/work/datasets/raw/Marxan_BLM_2/Marxan_BLM_source/pu/pulayer.shp: No such file or directory",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mCPLE_OpenFailedError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32mfiona/_shim.pyx\u001b[0m in \u001b[0;36mfiona._shim.gdal_open_vector\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mfiona/_err.pyx\u001b[0m in \u001b[0;36mfiona._err.exc_wrap_pointer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mCPLE_OpenFailedError\u001b[0m: /home/jovyan/work/datasets/raw/Marxan_BLM_2/Marxan_BLM_source/pu/pulayer.shp: No such file or directory",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mDriverError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-4d9430b91820>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mblm_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBLM_calibration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMARXAN_FOLDER\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMARXAN_INPUTDATA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrid_file_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-6-ef78e3727cab>\u001b[0m in \u001b[0;36mBLM_calibration\u001b[0;34m(path, MARXAN_INPUTDATA, grid_file_path, Plot)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mBLM_calibration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mMARXAN_INPUTDATA\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrid_file_path\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPlot\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m->\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mblm_df\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'folder'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'blm'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mblm_folder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblm_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreateBlmProject\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrid_file_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mMARXAN_INPUTDATA\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'input.dat'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mblm_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'folder'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblm_folder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-4-bc4a418923d8>\u001b[0m in \u001b[0;36mcreateBlmProject\u001b[0;34m(path, grid_file_path)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m### RUN MARXAN several times\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mblm_range\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mpu_area\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid_file_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0marea\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpu_area\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'geometry'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marea\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mblm_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0melement\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marea\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0melement\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mblm_range\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/geopandas/io/file.py\u001b[0m in \u001b[0;36m_read_file\u001b[0;34m(filename, bbox, mask, rows, **kwargs)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mfiona_env\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_bytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m             \u001b[0;31m# In a future Fiona release the crs attribute of features will\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/fiona/env.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    406\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlocal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_env\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 408\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    409\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/fiona/__init__.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode, driver, schema, crs, encoding, layer, vfs, enabled_drivers, crs_wkt, **kwargs)\u001b[0m\n\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m             c = Collection(path, mode, driver=driver, encoding=encoding,\n\u001b[0m\u001b[1;32m    257\u001b[0m                            layer=layer, enabled_drivers=enabled_drivers, **kwargs)\n\u001b[1;32m    258\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/fiona/collection.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, path, mode, driver, schema, crs, encoding, layer, vsi, archive, enabled_drivers, crs_wkt, ignore_fields, ignore_geometry, **kwargs)\u001b[0m\n\u001b[1;32m    160\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    163\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWritingSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mfiona/ogrext.pyx\u001b[0m in \u001b[0;36mfiona.ogrext.Session.start\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mfiona/_shim.pyx\u001b[0m in \u001b[0;36mfiona._shim.gdal_open_vector\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mDriverError\u001b[0m: /home/jovyan/work/datasets/raw/Marxan_BLM_2/Marxan_BLM_source/pu/pulayer.shp: No such file or directory"
     ]
    }
   ],
   "source": [
    "blm_df = BLM_calibration(MARXAN_FOLDER, MARXAN_INPUTDATA, grid_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>folder</th>\n",
       "      <th>blm</th>\n",
       "      <th>cost</th>\n",
       "      <th>boundary_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BLM_0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1342200.0</td>\n",
       "      <td>14540000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BLM_0.01</td>\n",
       "      <td>0.020</td>\n",
       "      <td>1344440.0</td>\n",
       "      <td>11836000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BLM_0.1</td>\n",
       "      <td>0.200</td>\n",
       "      <td>1384390.0</td>\n",
       "      <td>5232000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BLM_1</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1644050.0</td>\n",
       "      <td>3592000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BLM_10</td>\n",
       "      <td>20.000</td>\n",
       "      <td>1846320.0</td>\n",
       "      <td>2816000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BLM_100</td>\n",
       "      <td>200.000</td>\n",
       "      <td>1987630.0</td>\n",
       "      <td>2452000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      folder      blm       cost  boundary_length\n",
       "0  BLM_0.001    0.002  1342200.0       14540000.0\n",
       "1   BLM_0.01    0.020  1344440.0       11836000.0\n",
       "2    BLM_0.1    0.200  1384390.0        5232000.0\n",
       "3      BLM_1    2.000  1644050.0        3592000.0\n",
       "4     BLM_10   20.000  1846320.0        2816000.0\n",
       "5    BLM_100  200.000  1987630.0        2452000.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotBLM(MARXAN_FOLDER, grid_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
